Grow p-type MoS₂ on FeNC for CO₂ Sensing in Complex Environments with Intelligent Recognition

A wealth of theoretical studies demonstrates p-type MoS₂ (p-MoS₂) as a promising candidate for carbon dioxide (CO₂) detection at room temperature. Its applications are retarded by issues associated with its practical chemical synthesis and sensing selectivity. Herein, a chemically tunable strategy is established for in situ growth of p-MoS₂ with controlled thickness and n-/p-type transition on N- and Fe-enriched carbon (FeNC) nanosheets. The introduced sulfur vacancies (S_vacs) enhance the sensitivity to CO₂, and the modulated electron distribution suppresses surface oxygen ionization to improve sensing selectivity. The optimized p-type composites can detect CO₂ fluctuation levels as low as 50 ppm at room temperature. Density functional theory (DFT) and grand canonical Monte Carlo (GCMC) simulations clarify the underlying mechanisms. A visualized machine learning (ML) model is developed using a hybrid ML strategy that generates regression surfaces from linear/nonlinear data. Through this model, a single sensor accurately discriminates CO₂ from interfering and predicts its concentration and humidity with accuracies exceeding 95%. An intelligent sensing system capable of environmental monitoring and tracking exhaled CO₂ is demonstrated. The measured fluctuations strongly correlate with physiological indicators, underscoring their potential for non-invasive health monitoring and medical diagnostics.